Generating weighted and thresholded gene coexpression networks using signed distance correlation
Generating weighted and thresholded gene coexpression networks using signed distance correlation
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DOI：
10.1101/2021.11.15.468627
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发表时间：
2021-11
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通讯作者：
Javier Pardo-Diaz;P. Poole;Mariano Beguerisse-Díaz;C. Deane;G. Reinert
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文献类型：
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作者：
Javier Pardo-Diaz;P. Poole;Mariano Beguerisse-Díaz;C. Deane;G. Reinert
即使在经过充分研究的生物体中，许多基因也缺乏有用的功能注释。产生这种功能信息的一种方法是使用包括功能注释的基因共表达数据网络来推断基因或蛋白质之间的生物关系。符号距离相关性已被证明对构建未加权的基因共表达网络很有用。然而，将关联值转换为未加权的网络可能会导致与关联强度相关的重要生物信息的丢失。这里介绍了一种利用符号距离相关性构建加权基因共表达网络的原则性方法。这些网络只包含那些相关值高于给定阈值的基因对之间的加权边。我们分析了不同生物体的数据，发现基于符号距离相关的网络比基于皮尔逊相关的网络更稳定，更能捕捉到更多的生物信息。此外，我们还证明了基于相同度量的带符号距离相关网络比未加权网络捕获了更多的生物信息。虽然我们使用生物数据集来说明该方法，但该方法具有通用性，可以用于构建其他领域的网络。数据和代码可用性https://github.com/javier-pardodiaz/sdcorGCN
Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming correlation values into unweighted networks may lead to a loss of important biological information related to the intensity of the correlation. Here introduce a principled method to construct weighted gene coexpression networks using signed distance correlation. These networks contain weighted edges only between those pairs of genes whose correlation value is higher than a given threshold. We analyse data from different organisms and find that networks generated with our method based on signed distance correlation are more stable and capture more biological information compared to networks obtained from Pearson correlation. Moreover, we show that signed distance correlation networks capture more biological information than unweighted networks based on the same metric. While we use biological data sets to illustrate the method, the approach is general and can be used to construct networks in other domains. Data and code availability https://github.com/javier-pardodiaz/sdcorGCN